Why MLOps manages ML lifecycle in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to show the first step in the ML lifecycle managed by MLOps.

ML Python

def ml_lifecycle_step():
    return '[1]'

step = ml_lifecycle_step()
print(step)

Drag options to blanks, or click blank then click option'

AData Collection

BModel Monitoring

CModel Deployment

DModel Retraining

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing Model Deployment as the first step.

The first step in the ML lifecycle is Data Collection. MLOps manages this to ensure quality data is available for training.

2fill in blank

medium

Complete the code to represent the MLOps task that ensures the model works well after deployment.

ML Python

def mlops_task():
    return '[1]'

task = mlops_task()
print(task)

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AFeature Engineering

BData Labeling

CModel Monitoring

DData Cleaning

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing monitoring with data cleaning.

Model Monitoring is the MLOps task that checks if the model performs well after deployment.

3fill in blank

hard

Complete the code to show the MLOps step responsible for updating the model with new data.

ML Python

def update_model():
    step = '[1]'
    print(f"Current step: {step}")

update_model()

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AModel Evaluation

BModel Deployment

CData Collection

DModel Retraining

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing Model Deployment instead of retraining.

Model Retraining is the step where the model is updated with new data to keep it accurate.

4fill in blank

hard

Fill both blanks to complete the dictionary that maps MLOps steps to their descriptions.

ML Python

mlops_steps = {
    'Data Collection': '[1]',
    'Model Monitoring': '[2]'
}

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AGathering and preparing data

BChecking model performance after deployment

CTraining the model

DDeploying the model

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up deployment with monitoring.

The dictionary maps Data Collection to gathering data and Model Monitoring to checking performance after deployment.

5fill in blank

hard

Fill all three blanks to complete the code that creates a summary of MLOps lifecycle steps and their order.

ML Python

mlops_lifecycle = {
    1: '[1]',
    2: '[2]',
    3: '[3]'
}

for step_num, step_name in mlops_lifecycle.items():
    print(f"Step {step_num}: {step_name}")

Drag options to blanks, or click blank then click option'

AData Collection

BModel Deployment

CModel Monitoring

DModel Retraining

Attempts:

3 left

💡 Hint

Common Mistakes

Putting retraining before deployment.

The typical MLOps lifecycle starts with Data Collection, then Model Deployment, followed by Model Monitoring.